Many recent approaches aim to learn deep invariant feature representations: some focus on domain adaptation by finding a representation whose distribution is invariant across source and target
distributions [1]}, [2]}, while others focus on conditional domain-invariance [3]}, [4]}. However, there is evidence that domain adaption approaches are insufficient when the test distribution is unknown and may lie outside the convex hull of training distributions [5]}, [6]}, [7]}.
